2017
Publication Year
2020-09-07T14:03:48Z
Acceptance in OA@INAF
The Grism Lens-amplified Survey from Space (Glass). IX. The Dual Origin of
Low-mass Cluster Galaxies as Revealed by New Structural Analyses
Title
Morishita, Takahiro; Abramson, Louis E.; Treu, Tommaso; Vulcani, Benedetta;
Schmidt, Kasper B.; et al.
Authors
10.3847/1538-4357/835/2/254
DOI
http://hdl.handle.net/20.500.12386/27180
Handle
THE ASTROPHYSICAL JOURNAL
Journal
835
The Grism Lens-ampli
fied Survey from Space (Glass). IX. The Dual Origin of Low-mass
Cluster Galaxies as Revealed by New Structural Analyses
Takahiro Morishita1,2,3, Louis E. Abramson1, Tommaso Treu1, Benedetta Vulcani4, Kasper B. Schmidt5, Alan Dressler6, Bianca M. Poggianti7, Matthew A. Malkan1, Xin Wang1, Kuang-Han Huang8, Michele Trenti4, Maruša BradaČ8, and Austin Hoag8
1
Department of Physics and Astronomy, UCLA, 430 Portola Plaza, Los Angeles, CA 90095-1547, USA;mtaka@astro.ucla.edu
2
Astronomical Institute, Tohoku University, Aramaki, Aoba, Sendai 980-8578, Japan 3
Institute for International Advanced Research and Education, Tohoku University, Aramaki, Aoba, Sendai 980-8578, Japan 4
School of Physics, The University of Melbourne, VIC 3010, Australia 5
Leibniz-Institut für Astrophysik Potsdam(AIP), An der Sternwarte 16, D-14482 Potsdam, Germany 6
The Observatories of the Carnegie Institution for Science, 813 Santa Barbara Street, Pasadena, CA 91101, USA 7
INAF-Astronomical Observatory, I-35122 Padova, Italy 8
University of California Davis, 1 Shields Avenue, Davis, CA 95616, USA
Received 2016 July 1; revised 2016 December 6; accepted 2016 December 8; published 2017 February 1 Abstract
Using deep Hubble Frontier Fields imaging and slitless spectroscopy from the Grism Survey from Space, we study 2200 cluster and 1748field galaxies at 0.2 z 0.7 to determine the impact of environment on galaxy size and structure at stellar masseslogM* M>7.8, an unprecedented limit at these redshifts. Based on simple assumptions
—re=f M( *)—we find no significant differences in half-light radii (re) between equal-mass cluster or field systems.
More complex analyses—re=f M( *,U-V n z, , ,S)—reveal local density (Σ) to induce only a 7%±3% (95% confidence) reduction in rebeyond what can be accounted for by U−V color, Sérsic index (n), and redshift (z) effects.
Almost any size difference between galaxies in high- and low-density regions is thus attributable to their different distributions in properties other than environment. Indeed, wefind a clear color–recorrelation in low-mass passive
cluster galaxies(logM* M<9.8) such that bluer systems have larger radii, with the bluest having sizes consistent
with equal-mass star-forming galaxies. We take this as evidence that large-relow-mass passive cluster galaxies are
recently acquired systems that have been environmentally quenched without significant structural transformation (e.g., by ram pressure stripping or starvation). Conversely, ∼20% of small-relow-mass passive cluster galaxies appear to
have been in place since z3. Given the consistency of the small-re galaxies’ stellar surface densities (and even
colors) with those of systems more than ten times as massive, our findings suggest that clusters mark places where galaxy evolution is accelerated for an ancient base population spanning most masses, with late-time additions quenched by environment-specific mechanisms mainly restricted to the lowest masses.
Key words: galaxies: clusters: general– galaxies: elliptical and lenticular, cD – galaxies: evolution – galaxies: structure Supporting material: machine-readable table
1. Introduction
In terms of a host of properties—color, star formation activity, structure, morphology—clusters harbor different galaxy popula-tions than average (“field”) environments (e.g., Hubble & Humason 1931; Dressler 1980). The mechanisms that produce these differences have been the subject of intense scrutiny. While evidence of environmental effects has been seen (e.g., Vollmer et al. 2009; Abramson et al. 2011; McPartland et al. 2016; Poggianti et al.2016), their roles and relative importance compared to in situ galaxy evolution remain poorly understood. Indeed, the extent to which clusters are agents that halt galaxy evolution, as opposed to tracers of regions where it has been accelerated, is still under debate (see Peng et al. 2010with Dressler1980; Thomas et al.2005; Guglielmo et al.2015; Abramson et al.2016).
One confounding factor is that galaxy-by-galaxy analyses reveal almost no differential environmental effects for systems, e.g., at fixed stellar mass ( *M ) and color (Grützbauch et al.2011). That is, while galaxy populations are different in low- and high-density regions, representatives of all parts of parameter space seem to exist everywhere(e.g., Dressler et al.2013,2016; Wu et al.2014).9This
appears to hold even for scaling laws that (seemingly) should contain the signatures of any transformational mechanism, such as the star formation rate–mass and size–mass relations (e.g., Maltby et al. 2010; Peng et al. 2010; Huertas-Company et al. 2013; Koyama et al.2013; Allen et al.2016; but see Vulcani et al.2010; Paccagnella et al.2016, who define environment by spectroscopic membership as opposed to spatial overdensity).
However, a key obstacle to many previous investigations has been their relatively high mass limits of logM* M10. In
this regime, a system’s self-gravity is strong, perhaps protecting it from environmental influences such as ram pressure stripping or harassment(e.g., Dressler & Gunn1983; Moore et al.1996; Treu et al. 2003; Lin et al. 2014). Furthermore, high-mass galaxies might be subject to internal processes—such as feedback from active galactic nuclei, or the suppression of star formation by morphological structures—that act before they enter the cluster, preventing the latter from having any effect at all (e.g., Martig et al. 2009; Hopkins et al. 2014). To better constrain the physical processes causally related to environ-mental density, targeting the low-mass tail of the Galaxy population(logM* M10) is key.
Some studies of the nearest clusters have probed this regime: Misgeld & Hilker (2011, see also Ferrarese et al. 2006) examined the size–mass relation of Local Group and Coma © 2017. The American Astronomical Society. All rights reserved.
9
Excluding the very most- and very least-massive red objects—dwarf ellipticals and cDs/BCGs—which may never exist in isolation (Koester et al.2007; Geha et al.2012).
galaxies at logM* M6, and Lisker et al. (2009) and
Toloba et al. (2015) explored the diversity of dwarf galaxies (dEs, dSphs) in Virgo, with the latter study using kinematical/ chemical information to find the evidence of ram pressure stripping-influenced evolution. However, at z»0, cluster galaxies are so uniformly old that the residual signatures of any transformational mechanisms may be detectable only in the most detailed fossil evidence(McDermid et al.2015). Shifting focus toz~0.5 would alleviate this issue by probing an epoch when clusters were still rapidly assembling, and hence more dramatically reshaping their galaxy populations (Butcher & Oemler 1978). The recent advent of ultra-deep multi-band imaging and spectroscopy from space enables such studies of low-mass, mid-z galaxies for thefirst time.
In this paper, we use data from the Hubble Frontier Fields (HFF; Lotz et al.2016) and Grism Survey from Space (GLASS; Schmidt et al. 2014a; Treu et al. 2015) to examine the Galaxy populations of clusters and thefield at0.2 z 0.7 to a hitherto unexplored mass limit oflogM* M>7.8.
We exploit these data to study the dependence of galaxy size on stellar mass and other structural properties as a function of environmental density using an unprecedented sample of over 3900 cluster and field galaxies. We examine these correlations because: (1) processes that depend on environment—e.g., ram pressure stripping, or mergers (rarer in richer systems)—affect galaxy size and structure in significant and well-defined ways (van Dokkum et al.2010; Damjanov et al.2011; Newman et al.2012; Nipoti et al.2012; Patel et al.2013, and many others), and (2) the depth and resolution of new HST imaging enables analyses of galaxy structure that current ground-based observations cannot support. This is especially true in the near-infrared, which most directly probes galaxies’ stellar mass distributions.
By using a new multidimensional approach that holistically examines galaxies in their natural parameter space—spanning color, size, structure, environmental density, and redshift—our analysis provides a new look at both rapid and long-term environmental influences, yielding a “4D” view of the Galaxy population at unexplored masses and spatial resolutions.
We organize our discussion around the central question posed above: how many of the observed differences in cluster/ field populations reflect phenomena driven by clusters versus those traced by them?
Ultimately, our results suggest that, while a cluster-specific process similar to ram pressure stripping is indeed operational now, ∼20% of present-day passive cluster galaxies with
* <
M M
log 10 must have been “built into” the cluster population at very early times. These findings support a scenario in which clusters mark places where galaxy evolution has been accelerated compared to—but not radically divergent from—the cosmic mean, but are now also in a phase of transforming mainly low-mass systems via environmentally specific phenomena.
We proceed as follows: in Section 2, we describe the observations and measurements upon which our analysis is based. In Section 3, we explore the canonical size–mass relations of our sample and use these to identify important spatiotemporal trends in the data. In Section4, we adopt a new framework to reinterpret galaxy structural parameters holisti-cally across all environments, performing a multidimensional analysis similar in spirit to the approach that led to the discovery of the fundamental plane(Djorgovski & Davis1987; Dressler et al. 1987) and the more-fundamental plane (Bolton
et al. 2007; Auger et al. 2010) of early-type galaxies. We discuss our results in Section 5 and summarize in Section6. Details of various parts of our analysis are also provided in Appendices.
Magnitudes are quoted in the AB system(Oke & Gunn1983; Fukugita et al. 1996). We assume W = 0.3m , W =L 0.7,
= -
-H0 70 km s 1Mpc 1, and a Chabrier (2003) initial mass function (IMF). The catalog for galaxy structural parameters are made available as an electronic table associated with this paper and through the GLASS website.10
2. Data
2.1. Imaging and Spectroscopy
We base our analysis on HFF imaging and GLASS HST spectroscopy for thefirst four HFF clusters with complete data: Abell 2744 (z=0.308), MACS0416 (0.396), MACS0717 (0.548), and MACS1149 (0.544). HFF imaging spans ACS F435/606/814W through WFC3IR F105/125/140/160W filters (seven bands), reaching a 5-σ limiting point-source depth of mF160W»28.7 (Kawamata et al. 2016; Lotz et al. 2016). Both programs use a parallel strategy where WFC3IR and ACS are exposed simultaneously so that the former falls on the cluster core(hereafter “CLS”) and the latter on a low-density infall/field region (“PR1”). HFF provided WFC3IR(ACS) follow-ups on PR1 (CLS), so all photometric data are available in both regions.
GLASS spectroscopy consists of 10-orbit G102+4-orbit G141 WFC3 grism observations covering the CLS pointings (containing the vast majority of our cluster sample) from which we derive spectroscopic redshifts(zspec). All PR1 redshifts are
photometric (zphot; Section 2.3). All GLASS spectra are
visually inspected for quality. Here, we make use only of “high quality” redshifts; i.e., those with quality flag fQ 3as described in Schmidt et al.(2014a) and Treu et al. (2015).
It is worth noting that, due to the limited WFC3IR field of view, our observations probe only the cores of clusters, i.e., out toRcl~R500~0.4 Mpcatz~0.5. These are, in some sense, the most extreme galaxy environments in the universe, and the locations where environmental processes (e.g., ram pressure stripping) are most effective. At the same time, PR1 observations sample close to mean-density environments(“the field”), at least at all redshifts distinct from that of the CLS cluster. Hence, our sample exhibits almost maximal density contrast, so our analysis should be quite sensitive to environmental effects.
To increase ourfield sample size, we also include multi-band HST imaging conducted by the eXtreme Deep Field (XDF) team (Illingworth et al. 2013). The XDF encompasses one WFC3IR pointing in GOODS-south, which we use to extend ourfield galaxy sample. These data are of comparable depth to the HFF and include the F775W and F850LPfilters in addition to the HFF complement. Ground-based spectroscopic and 3D-HST grism redshifts (van Dokkum et al. 2013; Momcheva et al.2016) are incorporated where available.
2.2. Photometry and Catalog Construction
After PSF-matching all images to F160W resolution (FWHM = 0. 18), we stack the data, weighted by rms, to maximize detections of faint (low-mass) galaxies. This composite image is then run through SExtractor (Bertin & 10
http://glass.astro.ucla.edu
2
Arnouts1996) as the detection image. After removing the intra-cluster light(ICL; see AppendixA), photometry is conducted on the individual images based on the detection locations.
To maximize signal-to-noise ratios(S/N) and thus optimize
zphotestimates,fluxes in each filter are determined within fixed
apertures of 12 pixels (0 7) in diameter. When estimating absolute quantities—such as stellar masses—these measure-ments need to be scaled to account for light outside the aperture. We do this by adoptingFLUX_AUTO from SExtractor as the total flux of each galaxy (see Section 2.5). All photometry is corrected for galactic extinction using the Schlegel et al.(1998) dust maps.11
Below, we analyze only objects with mF160W<26mag. Most of this sample hasS NF160W>8, sufficient for accurate stellar mass and structural parameter estimates (Schmidt et al. 2014b, see AppendixD). We have verified that our results are quantitatively robust to this cut-off. The magnitude criterion corresponds to a mass completeness limit oflogM* M~7.8
(Section 2.5).
2.3. Spectroscopic and Photometric Redshifts
Wherever available, we adopt public spectroscopic redshift (zspec) provided by several authors (Owers et al.2011; Ebeling
et al.2014; Balestra et al.2015).12We supplement these with GLASS zspec. As mentioned, we include only GLASS
redshifts with fQ 3, corresponding to two or more line detections(e.g., [OIII] and Hα; Treu et al.2015). These cover 7% of the mF160W <26 sample (269 galaxies). GLASS redshifts show excellent agreement with the ground-based measurements.
For galaxies lacking zspec, we derive photometric redshift
(zphot) using the seven-band HST photometry discussed above.
Wefit all spectral energy distributions (SEDs) using the EAZY code (v.1.01; Brammer et al. 2008), implemented with emission line/dusty spectrum templates based on the recipe of Ilbert et al.(2009). Given the depth of the HFF data, 85% of our sample has 3-σ detections in more than four bands, supporting reliable zphotestimates.
2.3.1. Photo-z Priors
The only modification we make to the default EAZY fitting routine is to apply different priors for CLS and PR1 objects. In PR1, where field galaxies dominate, we apply the default EAZY F160W prior derived from Theoretical Astrophysical Observatory(TAO) lightcones (Bernyk et al.2016).13In CLS, we modify this prior in order to account for the existence of each cluster as follows.
s = ´ + - ´ p z m z f p z m f g z z , 1 , , 1 F160W cls F160W fld cls ( ∣ ) ( ∣ ) ( ) ( ∣ ) ( )
where p z m( ∣ F160W fld) is the default(field) EAZY prior, f is the
fraction of field versus cluster galaxies at a given magnitude (Equation (8)), andg z ,( cls s) is a normalized Gaussian centered
at the redshift of a given cluster (zcls) with a dispersion
s =2000 km s-1(about twice the velocity dispersion of each
cluster). We set f to the number of galaxies in the PR1 pointing over that in the CLS pointing in bins of mF160W, assuming that
the CLS sample is dominated by cluster members. AppendixB provides further details.
We adopt thez_peak EAZY output as our zphot estimate.
We compare the best-fit zphot to zspec in Figure 1, finding a
median offset of á(zspec-zphot) (1 +zspec)ñ = 0.003, and a median absolute deviation dzphot=0.0073 (i.e., 0.7%) for galaxies at z<1.0. The cluster zphot prior is partially
responsible for this tight dispersion, but dzphot rises to just
1.8% in the PR1 and XDF fields where it is not employed, giving us high confidence in the accuracy of our zphotestimates.
Catastrophic outliers are defined to have ∣zspec-zphot∣
+z >
1 spec 0.1;
( ) they comprise 7.3% of the zspecsample.
After culling to0.2 z 0.7—a redshift range bracketing
the four HFF clusters—our final catalog consists of 3948galaxies, with 2200 in clusters and 1748 in field environments. A total of 298 have ground-based and 168 have GLASS spectroscopic redshifts.
2.4. Cluster and Field Sample Selection
Cluster members are identified using the redshifts described above. As spectroscopic and photometric estimates have different uncertainties, we define different criteria for Figure 1.Comparison between photometric(zphot) and spectroscopic redshifts (zspec) for 518 objects at <z 1.0. Spectroscopic redshifts are taken from ground-based measurements if available(red points), and from the GLASS catalog otherwise (blue circles; 242 galaxies with quality flag fQ3). Catastrophic outliers have∣zspec-zphot∣ (1+zspec)>0.1 and lie above or below the dashed lines. These were excluded from the calculation of the median offset, Dz, and the normalized median absolute deviation, dz, of the quantity(zspec-zphot) (1+zspec). The gray shaded region corresponds to
d ´ z
3 . Vertical bands mark the redshifts of the four clusters analyzed with widths corresponding to their velocity dispersions (~2000 km s-1). Our photometric redshifts are in excellent agreement with zspec for both cluster andfield galaxies.
11
https://ned.ipac.caltech.edu/forms/calculator.html
12http://www.stsci.edu/hst/campaigns/frontier-fields/FF-Data 13
membership based on the metric: d º -+ z z z z 1 . 2 incl cls cls ∣ ∣ ( ) Members have:
1. dzincl0.0084 or 0.0087 for ground- or space-based
zspec, respectively, corresponding to double the typical
cluster velocity dispersion (i.e., ~2000 km s-1),
con-volved with measurement errors.
2. dzincl0.0219for zphot, corresponding to3´ zdphot(see
Section 2.3).
We verified that the selected cluster galaxies mostly belong to the red sequence down to mF160W~25mag in the color– magnitude diagram (not shown), giving us confidence in the selection. Figure2 shows the sample’s redshift distribution.
2.5. Stellar Masses
Stellar masses for all galaxies are derived from their multi-band photometry (SED fitting), and zspec or zphot estimate
(Section 2.3), using FAST (Kriek et al. 2009). This process requiresfluxes to be scaled from aperture measurements (F ;aper
see Section 2.2) to the total values (Ftot) assuming:
= ´
F F F
F , 3
tot aper auto
F160W
aper
F160W ( )
where Fauto F160W
is the totalflux (FLUX_AUTO) from SExtractor, covering 3Kron radii (Kron1980).
This procedure works for 92% of the sample, but for the rest theFLUX_AUTO uncertainties are large enough (mainly due to close, bright neighbors or ICL residuals) that using it risks introducing a bias. In these cases—S N Fauto 1
F160W
( ) —we
apply no scaling. This affects stellar mass estimates very little
as the original 0. 7 aperture encompasses ~ r 2 e for most of
these systems.
We use the stellar population models of Bruzual & Charlot (2003), assuming solar metallicity, a Chabrier (2003) IMF, and a Calzetti et al.(2000) dust law. Internal extinction is calculated assuming0 AV 4mag with a grid spacing of 0.1 mag. We adopt exponentially declining star formation histories —SFR( )t µexp(-t t)—with logt yr-1Î[8, 10] in steps of 0.2 dex. Uncertainties are taken as the 1-σ limits derived by FAST.
2.6. Rest-frame Colors
We wish to examine how environment affects both galaxy structure and star formation. An efficient means of classifying galaxies by their star formation state is by their location in rest-frame U−V/V−J (“UVJ”) color–color space (Williams et al.2009). These colors are directly calculated by convolving the best fit EAZY spectral templates with Johnson U V, , and 2MASS J-band filters. We follow Williams et al. (2009) and define red (quiescent) galaxies to have:
- > ´ - + - > - < U V V J U V V J 0.88 0.69, 1.3, 1.6. 4 ( ) ( ) We refer to all others as blue(star-forming) galaxies.
Figure3shows the distribution of our sample—split by mass (logM M* 9) and environment—in UVJ space. We see not only the bimodality of passive/star-forming galaxies, but also the trend of increasing passive fraction with stellar mass, as expected. We note also that, at low stellar mass, cluster passive galaxies appear slightly redder than theirfield counterparts. We return to this point in Section5.2.
2.7. Cross-checking Photo-z Accuracy: Galaxy Number Counts and Passive/Star-forming Fractions
Figure 4, top, shows the stellar mass distributions of our samples. Our purpose here is not to investigate, e.g., the best-fit Schechter parameters, but rather to demonstrate the robustness of our largely zphotbased cluster/field sample separation.
Comparing ourfield results (right) with those from Muzzin et al.(2013) in a similar redshift range (0.2< <z 0.5), we see
good consistency atlogM* M8.3, the completeness limit
of the previous study. This is true for all galaxies, and both passive and star-forming sub-classes, suggesting that ourfield sample is indeed representative of the general galaxy popula-tion and not excessively contaminated by cluster objects.
Figure4, bottom-left, shows the fraction of passive galaxies for cluster andfield environments as a function of stellar mass. There is a significant excess in clusters over the entire mass range, rising from ~50% at logM* M»8 to over 90% at
* >
M M
log 10. This is also as expected from previous spectroscopic studies (e.g., Dressler et al. 2013, their Figure 16), and suggests that our cluster sample is not excessively diluted byfield galaxies.
Interestingly, the bottom-right panel in Figure4 shows that, even within our sample’s rather narrow redshift range (0.3zcls0.6), evolution in the cluster passive fraction— the Butcher & Oemler (1978) Effect—is detected. The evolution is observed only for low-mass systems (logM* M<9.0), which might suggest that environmental effects are most pronounced for these systems. We combine Figure 2. Top: zphot distributions of galaxies in the four cluster-core(CLS)
pointings. Histograms are color coded by cluster, with black dashed lines indicating the spectroscopic mean redshift of each (zcls). Galaxies within
d
3 zphot.´(1+zcls) (horizontal bars) are classified as cluster members. Bottom: same as the top panel, but for PR1 sources.
4
this result with our inferred galaxy structural properties to develop a preferred scenario for the origin and evolution of these low-mass passive cluster systems in Section 5, but regardless: all of the above results suggest that ourfield/cluster galaxy sample selection process is accurate.
2.8. Structural Parameters
Galaxy structural parameters—half-light radii (re), axis ratios
( ºq semiminor/semimajor axis), and Sérsic indices (n)—are estimated by fitting single Sérsic profiles (Sérsic 1963) using GALFIT (Peng et al. 2002). Initial guesses for the relevant parameters derive from the SExtractor output.
Although we discuss only the F160W structural parameters here—corresponding to rest-frame wavelengths of~1.0 m atm
~
z 0.5—we perform fits in all HST bands to consistently
estimate the ICL properties(AppendixA). After subtracting the ICL from the original CLS image, we then re-estimate the structural parameters for those galaxies and adopt the second-round values. In PR1, we adopt the initial fitting results.
During fitting, we constrain centroids and magnitudes to within 3 pixels(in x and y) and 1 mag of the SExtractor input values. We also set1 <re pixel<150(0.4<re kpc<60at
~
z 0.5),0.1<n<8 (Sérsic index), and >q 0.2. “Success-ful” fits are those whose derived parameters fall within these limits. Failures are excluded from further analysis. Close-neighbors—objects with centroids within 6 of target galaxies —are fit simultaneously.
We also visually inspect outliers which reside 2-σ above/ below the size–mass relations of each population (see next section), and exclude 132 galaxies whose fits are substantially affected by proximity to very bright galaxies/belong to a blended pair, or have grossly distorted morphologies. Ourfinal catalog contains 2636 galaxies with robust structural
parameters. This corresponds to a mean success rate of ~68%, rising from ∼64% at logM* M~8.0 to >80% at
* ~
M M
log 10.0. AppendixDprovides further details of the fitting procedure. Structural properties, with SED fitting parameters, are shown in Table1and available online.
3. Canonical Size–mass Relation Analysis
While we will ultimately adopt a more sophisticated description(Section4), we begin our analysis of environment’s influence on galaxy size and structure by examining “canoni-cal” size–mass relations: i.e., by splitting the sample into four populations—passive/star-forming, cluster/field—and com-paring linearfits to theirlogre–logM* correlations. We model
the samples with a simple regression ⎛ ⎝ ⎜ ⎞ ⎠ ⎟ ⎛ ⎝ ⎜ ⎞ ⎠ ⎟ * a b s = + + r M M N log kpc log 10 , 5 e 9 ( ) ( )
whereα and β are the relation’s intercept (at109M) and slope,
and N ( ) is a Gaussian describing its intrinsic dispersions
assuming that sizes are lognormally distributed atfixed stellar mass(e.g., Newman et al. 2014).
We use standard Bayesian techniques to derive the posterior probability of the parameters—including the intrinsic scatter— using a Monte Carlo Markov Chain (MCMC) solver (see AppendixE).
Figure 3.Rest-frame UVJ diagrams for cluster(left) and field (right) galaxies, with masseslogM* M9(top) and <9 (bottom). Galaxies in the top-left box(dashed lines; from Williams et al.2009) in each panel are classified as
passive; the remaining galaxies we define as star-forming. A clear trend of increasing passive fraction with both stellar mass and environmental density emerges as expected, but we alsofind low-mass passive cluster galaxies to be redder than those in thefield.
Figure 4. Top: stellar mass functions of cluster members (left) and field galaxies(right), color-coded by population (black=all, blue=star-forming (SF), red=passive). Error bars assume a binomial distribution. Results from Muzzin et al. (2013) for field galaxies at 0.2< <z 0.5 are shown for comparison(colored consistently). Bottom left: fraction of passive galaxies as a function of stellar mass for cluster members(black) and field galaxies (gray). The Muzzin et al. (2013) field result is shown as the red solid line. Our
measurement diverges from those authors’ at the highest stellar mass, but our sample of such objects is very small. Bottom right: fraction of passive galaxies for each of our four clusters. The population of low-mass galaxies (logM* M<9) evolves even in the narrow redshift range these span (or ∼2 Gyr of cosmic time).
Table 1
GLASS Size–mass Relations: Source and Structural Catalog
IDcls IDid aJ2000 dJ2000 zbest F160W mag logM* M logre kpc logn logb a fGALFIT (U-V) mag S5thMpc−2
1 1225 3.5751052 −30.3789048 0.32 25.322 0.079 7.56 0.095 0.01 0.1262 0.9031 0.4286 −0.3188 0.1537 0 1.384 0.701 79.994 1 1227 3.5750756 −30.377077 0.31 18.429 0.0004 10.44 0.01 0.6893 0.05 0.5623 0.05 −0.4815 0.05 0 2.002 1.26 414.472 1 1228 3.57345 −30.3779341 0.31 19.837 0.0012 9.85 0.01 0.3876 0.0014 0.6609 0.0038 −0.2676 0.05 0 1.889 1.186 613.999 1 1231 3.5751362 −30.3785238 0.31 21.42 0.0038 9.12 0.025 0.6115 0.0076 0.6096 0.0171 −0.1024 0.0055 0 1.719 1.011 969.508 1 1537 3.5719139 −30.377392 0.31 21.362 0.0044 9.13 0.01 0.2536 99.0 0.1239 99.0 −0.2441 99.0 0 1.696 0.968 381.979 1 1694 3.5775421 −30.3788716 0.31 21.148 0.0031 9.31 0.01 0.1411 0.0002 0.1931 0.0028 −0.1871 0.05 0 1.825 1.064 652.593 1 1711 3.6095439 −30.3821106 0.29 17.929 0.0003 10.57 0.01 0.2158 0.0003 0.433 0.0016 −0.1367 0.05 0 2.069 1.417 10.539 1 1720 3.5898065 −30.3784113 0.33 24.616 0.0721 7.9 0.015 0.2149 0.0035 0.316 0.0147 −0.1487 0.0061 0 1.474 0.742 36.944 1 1756 3.5891816 −30.3789491 0.31 22.77 0.0122 8.58 0.04 0.1786 0.0006 0.233 0.0025 −0.0862 0.05 0 1.486 0.79 129.734 1 1763 3.5976371 −30.3792022 0.31 20.411 0.0027 9.6 0.01 0.323 0.0001 0.49 0.0014 −0.3768 0.05 0 1.824 1.14 110.436 1 1797 3.5788573 −30.3794304 0.31 23.845 0.023 8.15 0.035 0.0052 0.0018 0.0414 0.0118 −0.0088 0.0044 0 1.532 0.772 374.076 1 1804 3.579726 −30.3795369 0.31 23.316 0.0166 8.38 0.07 0.2024 0.0008 0.0792 0.0036 −0.2596 0.05 0 1.517 0.848 238.311 1 1823 3.571678 −30.3797062 0.31 23.53 0.0131 8.3 0.02 −0.2747 0.0012 0.2504 0.0098 −0.0506 0.0049 0 1.676 0.881 613.999 1 1830 3.5953954 −30.3804029 0.31 19.449 0.0008 9.92 0.01 0.5365 0.0003 0.4871 0.0014 −0.2676 0.05 0 1.719 1.056 271.209 1 1853 3.5792236 −30.380187 0.3 23.613 0.0224 8.21 0.01 0.2104 0.0011 −0.0132 0.0089 −0.1427 0.006 0 1.574 0.784 44.584
Note.(a) Cluster ID. 1: Abell2744CLS 2: MACS0416CLS 3: MACS0717CLS 4: MACS1149CLS 5: Abell2744PR1 6: MACS0416PR1 7: MACS0717PR1 8: MACS1149PR1 99: XDF. (b) ID for individual objects. (c) zbestis ground-based spectroscopic redshift if available, implemented with GLASS grism redshift, and photometric-redshift derived by EAZY for else.(d) F160W-band Auto magnitude derived by SExtractor. (e) Stellar
mass derived by FAST.(f) F160W-band structural parameters derived by GALFIT. (g) Visual inspection flag for sources s2 above/below the size-mass relation for each population. 0: Fine 1: Contaminated 2:Point sources.(h) Rest-frame -U V V, -Jcolors derived by convolving best-fit templates by EAZY. (i) Fifth-closest local galaxy number density. Only a portion of this table is shown here to demonstrate its form and content.
(This table is available in its entirety in machine-readable form.)
6 The Astrophysical Journal, 835:254 (22pp ), 2017 February 1 Morishita et al.
Wefit the samples over the entire mass range, but note that σ and all parameter uncertainties decrease when the sample is split atlogM* M~9.8(see Section3.3), and the low- and
high-mass objects fit separately (Table 2). This suggests that these populations may be fundamentally different. Sections3.4 and 5explore this statement further.
3.1. Size–mass Relations of Four Populations Figure 5 shows the derived linear size–mass relations (Equation (5)) for the four populations: passive and star-forming galaxies in clusters and the field. Outliers excluded from the analysis are plotted as open boxes (see Section 2.8, Appendix A). The best-fit slopes are derived with each point weighted by its measurement error(see AppendixE). Figure6 and Table 2summarize the results.
For either star-forming or passive galaxies, wefind identical slopes and intercepts in both cluster-core and field environ-ments within the uncertainties, suggesting—as previously seen (e.g., Huertas-Company et al.2013; Vulcani et al.2014)—that environment seemingly does not affect galaxy size (at fixed mass, population, and time) down to logM* M~8.
Furthermore, while we recover the expected correlations of stellar mass and Sérsic index(e.g., from Lang et al.2014) for both sets of galaxies (color-coding in Figure 5; see also Section 3.2), there is no obvious difference in Sérsic index trends in clusters or thefield, suggesting that environment also does not affect galaxy structure.
Notably, our derived slopes for passive systems are much shallower than those found previously in either the local universe (b ~ 0.6 by Shen et al. 2003) or at similar redshifts to our sample’s (b ~ 0.7 by van der Wel et al. 2014, fit over
*
M M
log 10). As detailed in Section 3.3, this finding is driven by our low mass-completeness limit—logM* M=
7.8—hitherto unexplored at ~z 0.5.
3.2. Structural Parameters on the Size–mass Diagram Besides size, galaxies’ structures can encode the action of evolutionary mechanisms. We now explore this aspect of our systems as parameterized by the Sérsic index, n.
Figure7shows the median and 16th–84th percentile spreads in n for passive/star-forming cluster/field galaxies as a function of stellar mass. As was true for their sizes (Section 3.1), star-forming cluster and field galaxies display almost identical trends, implying that entrance into or life inside the cluster (at late times) induces little if any structural transformation in these systems.
We also see that the Sérsic index distributions for low mass (logM* M<9.5) passive and star-forming galaxies overlap significantly in both environments, though passive galaxies— especially in clusters—are offset systematically to slightly higher n values. Such structural similarities of low-mass star-forming and passive galaxies supports a scenario where low-mass passive systems do not arise through violent mechanisms —such as mergers—but instead gentle phenomena. Alterna-tively (or additionally), they are most consistent with having been drawn exclusively from the high-n tail of the star-forming galaxy population, modulo the effects of disk fading. We return to these points in Section5.
3.3. Two Populations of Passive Cluster Galaxies The results above show that galaxies in the highest density regions of the universe do not differ systematically in the size– mass–Sérsic index plane from those in mean density environ-ments. However, this does not mean that clusters do not influence galaxy growth, nor does it mean that they do not trace special regions of the universe. The comparisons so far have been gross examinations of four samples at the same epoch. Yet, the mechanisms that either are transforming or have transformed the cluster population with respect to that of the field (Figure4) may act too subtly—or have acted too long ago —to probe in the above fashion. Can we find any evidence for this in our data?
To do so, we take two steps. First, we turn our attention exclusively to the low-mass tail of the cluster population: as mentioned in Section1, these systems should be most affected by environment-specific mechanisms (harassment, strangula-tion, stripping). Second, in the next section, we study the sizable (0.25 dex) scatter of the size–mass relation: it is clearly significant, so perhaps it encodes important information. Splitting the sample atlogM* M~9.8,14where the slope
seems to change, we fit the less- and more-massive galaxies separately with the formula in Equation(5). Figure8shows the results for passive cluster galaxies.
Here, we see that low-mass passive cluster galaxies exhibit a much milder, nearly flat slope of b =1 0.110.01. Put differently, galaxies in this regime increase in size by a factor of 1.7 for every factor of 100 in stellar mass. This is much shallower than either the slope for massive passive galaxies —b ~ 0.512 (consistent with previous studies; e.g., Shen
et al. 2003)—or star-forming galaxies of any mass in any environment (b ~ 0.2; e.g., van der Wel et al. 2014; Allen et al. 2016; Annunziatella et al. 2016). Notably, the inferred intrinsic scatter decreases from s =0.270.01 of the single slope fitting to s =1 0.200.01 for low-mass, and
s =2 0.230.01for high-mass galaxies when these systems arefit separately. All best-fit results are listed in Table2.
Thesefindings qualitatively support previous studies that find the low-mass passive population to have a shallower size–mass relation slope in the local universe (e.g., Binggeli et al. 1984; Ferrarese et al.2006; Omand et al. 2014) and at ~z 0.5 (e.g., van der Wel et al. 2014). Ferrarese et al. (2006) is especially relevant as it is a much more highly resolved HST study of Virgo cluster galaxies. These authors identify a similarly flat trend —dlog( )re dMB= -0.050.02b»0.125 for galaxies with MB -20 (logM* M10.5, assuming M LB from
Bell et al.2003, at B-R =1.4. See their Equation (21) and Figure 117, top-right). Later studies of Virgo dwarf galaxies by Lisker et al. (2009) and Toloba et al. (2015) confirm these findings. Ultimately, we will concur with van der Wel et al. (2014) and Toloba et al. (2015) in suggesting that some of these objects are likely environmentally stripped low-mass late-types (but also Lisker et al.2009in that some are not; Section5), but, irrespective of any mechanism, a break in this relation suggests the existence of two classes of passive galaxies with distinct formation paths: one with a steep size–mass slope indicative of nearly constant stellar surface density (b = 0.5; Hubble 1926) and therefore perhaps a single formation time, and one with a shallow slope(b » 0.11 ) indicative of a broad range of (mostly 14
Table 2
GLASS Size–mass Relations: Best-fit Coefficients
Region Type α β σ a1 b1 s1 a2 b2 s2
Single Slope Double Slope (Low-mass) Double Slope (High-mass)
Cluster Passive 0.150-+0.0080.009 0.169-+0.0080.012 0.270-+0.0060.006 0.109-+0.0060.006 0.111+-0.0140.009 0.197-+0.0070.005 -0.302+-0.0430.045 0.513-+0.0280.032 0.231-+0.0100.014 Cluster Star-forming 0.360-+0.0180.014 0.189-+0.0190.019 0.275-+0.0130.011 0.333-+0.0180.018 0.179+-0.0170.018 0.228-+0.0080.008 0.092-+0.1620.120 0.395-+0.0930.136 0.187-+0.0410.020 Field Passive 0.168-+0.0140.011 0.150-+0.0160.016 0.224-+0.0130.009 0.133-+0.0200.021 0.078+-0.0240.018 0.184-+0.0100.009 -0.067-+0.1330.134 0.340-+0.0910.091 0.213-+0.0310.016 Field Star-forming 0.373-+0.0130.010 0.223-+0.0140.012 0.251-+0.0060.004 0.350-+0.0130.011 0.210+-0.0150.014 0.216-+0.0070.004 0.343-+0.0730.082 0.225-+0.0660.063 0.197-+0.0200.019 Note.Summary of the best-fit coefficients for single slope (logre kpc=a+blog(M* 109M)+N( ) see Equations; (5) in the main text) and double-slope fit (same as Equation (5), but subscript 1 for
* <
M M
log 9.8 and subscript2 forlogM* M9.8 galaxies).
8 The Astrophysical Journal, 835:254 (22pp ), 2017 February 1 Morishita et al.
lower) stellar surface densities, and therefore a range of formation times.
We note that the similarly shallow slope is also observed for the passive field galaxies (Table 2), where environmental processes are thought to be subtle. In this case, the shallow slope for low-mass galaxies would not be attributed to the environmental effect. However, low-mass passivefield galaxies in this study could be in similar environments with cluster member galaxies, because we have grouped the sample into the two subgroups by using the same FoV. Actually, Geha et al. (2012) found that most low-mass passive galaxies are in group (or denser) environments by using the local sample, and so would be affected by processes similar to those affecting the low-mass cluster sample. Our genuine field sample, which resides in PR1 and XDF regions, is statistically weak and larger data sets will be required to fully address this question.
3.4. Toward a Formation Pathway: Star-forming Galaxies as a Model for Passive Galaxies
In any scenario, passive galaxies were star-forming at some epoch. Hence, we can use the star-forming galaxy size–mass relation derived above as a model for the sizes that passive galaxies should have if they had stayed in the star-forming population. Residuals from this exercise—the difference between how big the passive galaxies are and how big they are predicted to be—may contain important information about how, when, and therefore why they diverged from their star-forming peers.
Figure8 compares the size–mass relation of passive cluster galaxies with the mean relation for star-forming galaxies at several redshifts taken from van der Wel et al.(2014). Points are color-coded by the rest-frame U−V color. This quantity is a good indicator of the time since a galaxy’s last episode of star formation (at least out to ∼4 Gyr for low-mass objects, assuming the local stellar mass–metallicity relation of Kirby et al. 2013 holds at z~0.5) and therefore serves as a clock
counting back to when any red galaxy was last in the star-forming population. Evidently, only the largest of the passive low-mass systems have sizes compatible with equal mass star-forming (field) galaxies near the epoch of observation (0.2 z 0.7), and are therefore consistent with being drawn
from that population. The sizes of some very high mass passive systems are also comparable with those of star-forming galaxies at the same epoch, but since the former are giant ellipticals, effectively all of their other properties rule them out from having descended from the latter.
Figure 9 explores these findings in greater detail, showing the size difference of passive cluster galaxies from the star-forming relation at z~0.5 (taken from van der Wel et al. 2014). Boxes highlight the median U−V colors in
´
0.2 0.1 dex boxes. The horizontal lines also shown are the predicted offsets derived from the size-mass relations at different redshifts also from van der Wel et al. (2014).
A concern here is that, as seen in the left panel of Figure5, star-forming galaxies exhibit a color–mass trend, such that
more massive star-forming objects are redder. Hence, the observed color gradients in Figure 9 might reflect a baseline mass–color covariance in the star-forming (source) population, not a true third parameter.
To correct for this, we fit the mass–color relation for star-forming galaxies, obtaining:
⎛ ⎝ ⎜ ⎞ ⎠ ⎟ * - = + U V M M mag 1.03 0.11 log 109 . ( )6
As shown in the right panel of Figure9, even after applying this correction to the passive cluster galaxies, the color gradient along the y-axis remains, confirming our earlier statement that, to some degree, the spread of passive cluster galaxy sizes at fixed mass reflects a record of the time when a system left the star-forming population. As this extends to at leastz~3(i.e., ∼7 Gyr before the epoch of observation), this gradient can encode quite long timescales (Speagle et al.2014; Abramson et al.2016).
A clear, quantitatively robust color gradient is apparent at fixed stellar mass, shown in Figure 10. As clarified by the colored squares in Figure 9, smaller-size passive galaxies are also redder than larger-size passive galaxies. This finding amplifies results from Figure8: at low masses, the largest-size passive galaxies have both sizes and colors consistent with their having been been drawn from the star-forming population more recently than their smaller-size passive counterparts.
Taken together, Figures 8–10 present strong evidence that smaller passive galaxies“quenched” earlier, while the largest passive galaxies are quenching now. The same trend is observed in local clusters (e.g., Valentinuzzi et al. 2010, for
* >
M M
log 9.8 galaxies) and in the field (e.g., Poggianti et al.2013,logM* M>10.3), with luminosity-weighted age,
rather than U−V color, which support our interpretation. Interestingly, our analysis shows no such gradient for massive passive galaxies (logM* M>9.8; Figure 10),
supporting the idea that they have a different formation history than many low-mass objects, at least in the sense of having been in place long before the epoch of observation. This conclusion is also supported by the nearly uniform stellar surface densities of these objects (Figure 8; b ~ 0.52
corresponds toM* re =const.
2 ), compared to the large spread
of densities in the low-mass population. This homogeneity of massive passive galaxies is also found by Zanella et al.(2016) with HST spectroscopic measurements atz~2.
These facts—combined with the detailed properties of the massive galaxies’ stellar populations (Thomas et al. 2005; McDermid et al. 2015)—suggest an accelerated growth channel for the high mass population, rather than environ-mental quenching, which may dominate at low masses. The low-mass population is converged to a high-mass one at low Dlogre. This may indicate that these low-mass galaxies trace
the same channel as the high-mass galaxies. We return to this statement in Section5.
Table 3
GLASS Size–mass Relations: Best-fit Coefficients for the Holistic Fitting (Equation (7) and Figure11)
α bM* bn bUV bS5 bz σ -+ 0.122 0.0050.005 -+ 0.254 0.0080.007 -+ 0.041 0.0200.019 -+ 0.286 0.0140.014 --+ 0.0310.0070.006 --+ 1.059 0.1300.142 -+ 0.205 0.0030.003
Our ability to identify and compare this trend with non-cluster low-mass passive galaxies is hampered by the very small sample size. The same exercise reveals a trend in the same direction as in the cluster sample, but with large uncertainties. The mean color of the field systems is bluer than their cluster counterparts at these low masses, another point we will return to in Section 5.
4. A Holistic Analysis of All Galaxies in Higher-dimensional Parameter Space
So far, we have performed a canonical examination of the size–mass relation (Section 3). When separating samples by color and environment a priori, it seems that the relation is not so sensitive to where a galaxy is, but rather when it left the star-forming population (Figure9).
Technically, however, the above comparisons are not the fairest tests of environmental effects. These require matching cluster and field samples in all relevant characteristics—e.g., color, mass, and Sérsic index—leaving environment as the only distinguishing trait. Here, we adopt a multidimensional analysis that takes the above factors into account, and thus provides a fair, quantitative assessment of environmental effects.
Combining all galaxies into a single sample, we change our description from one in which mass is the only independent variable to one where a range of other parameters might also influence galaxy size, including: Sérsic index, U−V color, redshift, and local number density (i.e., environment). Rather than imposing somewhat artificial boundaries, this procedure allows for a data-driven exploration of the correlations between continuous parameters. The new multidimensional correlation becomes M ⎛ ⎝ ⎜ r ⎞⎠⎟= +N s log kpc , 7 e ( ) ( ) where M ⎜ ⎟ ⎜ ⎟ ⎛ ⎝ ⎜ ⎞⎠⎟ ⎛ ⎝ ⎞⎠ ⎛ ⎝ ⎜ ⎞ ⎠ ⎟ ⎛ ⎝ ⎞⎠ * * a b b b b b º + + + S + + + - -S - M M n z U V log 10 log 1.50 log 214.0 Mpc log 1 1.54 1.43 mag . M n z 9 5 2 UV 5 [( ) ]
Here,S =5 (5+1) pr52 is the projected number density(per
sq. Mpc) defined over the distance to the 5th closest object, and all variables arefit with respect to the pivot values (medians) in their respective denominators. Figure 11 shows the best-fit relation, whose parameters are listed in Table 3.
The derived coefficients reveal how much galaxy sizes depend on one parameter when the others are held fixed; they effectively describe how samples differ in one property when matched in all others.
From these results, we see that stellar mass and color have the strongest correlations with galaxy size: when logM* M
increases by 1.0 dex, logre kpc changes by bM*´1.0~
0.25 dex; nearly a factor of 2. Similarly, a 1.0 mag increase in
-U V
( ) color results in ∼0.29 dex decrease in size, consistent with the results in Figure9.15
In this context, the nearly complete independence of galaxy sizes on environmental density deduced from Figure6is made dramatically and quantitatively clear. The best-fit coefficient is
b = -S5 0.031, corresponding to only Dlogre kpc<0.1dex
(<26%) over the factor of ∼1000 spread in projected density probed by our data( 1 1000). We stress again that our sample spans normal environments to the densest regions in the universe—cluster cores. Hence, it seems unlikely that a stronger signal could be found by looking elsewhere.
We have confirmed that the residuals produced by applying the globally fit holistic model to the field and cluster subsamples separately areflat, and have a dispersion consistent with that shown in Figure11. Hence, cluster galaxies with low S5(e.g., those at large Rcl) and non-cluster galaxies with high
S5(e.g., those in groups), do not appear to deviate significantly
from expectations derived without knowledge of the global environment.
While these results are consistent with our previousfindings based on a simpler analysis, the treatment described here presents several advantages. First, it allows the data to identify which correlations are most important, rather than humans using bins based on what we think should be relevant. Second, by making all variables explicit, we avoid being misdirected by correlations imposed by hidden parameters.
Indeed, had such an analysis been favored a priori, it would have been immediately evident that any analysis of the effect of environment atfixed epoch would provide only partial answers. As shown in Figure9, most of the well known“environmental” trends are in fact a reflection of differences in galaxy ages, and perhaps ancient discrepancies in the mass functions that differentiate the structures that collapsed first in the universe (clusters) and those that do so much later (the field; see Figure 4, also Kelson et al. 2016). Beyond this, the gross appearance of such scaling relations is apparently highly insensitive to any cluster-specific mechanisms.
Combined with the residual color-dependence—a “clock” measuring the time since a passive galaxy left its star-forming peers—this finding reinforces suggestions from the previous analyses that local number density traces transformative phenomena to an important extent. That is, it marks regions wherein an initial large-scale overdensity caused all systems within it to evolve rapidly, independent of any late-time transformative effects. We discuss this further below.
5. Discussion
So far, we have studied galaxy size and structure in different environments by (1) splitting the sample into four sub-populations (the “canonical” approach; Section 3), and (2) treating them as a single population (the “holistic” approach; Section4).
Via thefirst approach, we see no environmental effect on the size–mass relation, even at the unexplored stellar mass limit of
* =
M M
log 7.8 at z~0.5. Via the second method, this finding is qualitatively confirmed, and quantitatively contex-tualized: assuming only that galaxies can be described by a 15
The largest absolute coefficient corresponds to redshift effects —b = -1.06z dex/dex—but, the small z range observed ensures this does
not translate to a large impact on measured sizes: a 0.3 dex( ´2 ) change in re
via redshift evolution alone requires comparingz~2 systems to our sample (consistent with Figure9).
10
suite of parameters that should be sensitive to the same phenomena impacting galaxy sizes, we find that environment has perhaps the smallest effect.
Figures 8–11suggest that, for many systems, by the time a galaxy is sufficiently “transformed”—by whatever process—to be identified as “passive,” evidence of any direct impact of cluster-specific phenomena is wiped out, or of secondary importance at best.
This seems certainly true for high-mass cluster galaxies (logM* M9.8), which dominate the cluster’s stellar mass content and have stellar populations that are completely incompatible with having been drawn recently from the star-forming population. For low-mass cluster galaxies, however, the picture is more nuanced.
As discussed in Section3.4(Figures9and10), the largest-re
passive cluster galaxies withlogM* M<9.8 have sizes and
Figure 5.F160W size–mass relation of SF and passive galaxies (left/right, respectively) in clusters and the field (top/bottom). Colors reflect galaxy F160W Sérsic indices (logn). Open squares denote systems with close, large/bright neighbors, which comprise 132 visually inspected outliers, and are excluded from further analysis(Section2.8). As Figure8shows, low- and high-mass passive galaxies have markedly different size–mass relations, hence, due to our inclusion of galaxies
with masses as low aslogM* M=7.8, the slopes we obtain for passive galaxies are∼0.3 dex per dex shallower than previous estimates (e.g., van der Wel et al.2014, overplotted as black dashes over theirfitting range). Size estimates are robust above gray zones at the bottom of all plots, showing re FWHMF160W 2at z=0.5 (Morishita et al.2014).
colors consistent with their having come from the field star-forming population at times close to the epoch of observation. The smallest-re galaxies in this population, however, seem to
have been in place for many Gyr, perhaps as long as their higher-mass neighbors, suggesting a dual formation scenario for low-mass passive galaxies(see also, e.g., Poggianti et al.2006).
5.1. Large-reLow-mass Passive Cluster Galaxies
On the face of it, the fact that the largest-size low-mass passive galaxies lie near the size–mass relation of star-forming galaxies at the same epoch points immediately to something like ram pressure stripping or starvation (e.g., Wetzel et al. 2013) as the most likely transformative mechanisms. These are indeed cluster-driven, relying exclusively on the properties of the mature cluster environment. Four additional facts support this conclusion.
1. Our target clusters are currently bright X-ray sources, confirming the presence of dense intra-cluster gas (Mantz et al.2010). Especially in the core regions probed by the HST observations, drag from this hot atmosphere can effectively strip gas from infalling galaxies, and also stifle their accretion of new gas for future star formation, the definitions of ram-pressure stripping and starvation. 2. Large-size, low-mass systems are most amenable to(ram
pressure) stripping as their internal gas supplies are most loosely bound(Treu et al.2003). They would also run out of fuel for star formation relatively quickly if starved of external fuel supplies given the generally higher specific star formation rates of their low-mass star-forming galaxy progenitors(e.g., Salim et al.2007; Whitaker et al.2014). 3. The low-mass end of the passive cluster mass function grows over the epochs probed (Figures4, bottom-right), consistent with a scenario where star-forming galaxies are continually being transformed. This is also consistent with previous studies that a single epoch is disfavored for the formation of low-mass passive cluster galaxies(e.g., Roediger et al.2011; Toloba et al.2014).
Figure 6.Comparison of the best-fit size–mass relations (Equation (5)) for the
four populations considered in Figure 3: cluster passive and star-forming galaxies(red/blue lines); field passive and star-forming galaxies (orange/green lines). Dashed lines show the inferred intrinsic dispersion of the relations, σ. Comparison of the best-fit parameters is shown in the inset. Contours reflect 68%, 96%, and 99% confidence intervals as determined using an MCMC solver.
Figure 7.Median and 16th–84th percentile spreads of F160W Sérsic index, n, for cluster star-forming/passive galaxies in bins of stellar mass. Background gray plots are those forfield star-forming/passive galaxies. Values for cluster galaxies are replotted in light gray in the right panel for comparison. Star-forming galaxies have similar n distributions at logM* M10, while passive galaxies display a monotonically rising trend.
Figure 8.Size–mass relation for red cluster galaxies. Points are color-coded by rest-frame U−V color. We separately fit low- and high-mass systems—split at
* = M M
log 9.8(vertical dashed line), where Sérsic index of star-forming galaxies deviate (Figure 7). The low-mass slope (gray line) is almost flat
(b = 0.111 ), while the high-mass fit (black line) is much steeper (b = 0.512 ). Size–mass slopes of star-forming galaxies by van der Wel et al. (2014) are
overlaid to discuss the star-forming population as a parent sample of passive galaxies.
12
4. The relatively low Sérsic indices of the low-mass passive cluster galaxies(Figure7) point to a “gentle” mechanism; one that does not destroy disks/rearrange galaxies’ stellar
components, which is consistent with starvation, ram pressure stripping, and galaxy harassment (Bialas et al. 2015). The observed Sérsic indices of low-mass passive galaxies are slightly higher than those of star-forming ones with similar stellar mass, but this difference is explained by disk fading, where cessation of star formation in the disk leads to more concentrated light profiles (and thus higher Sérsic indices; Lackner & Gunn2013).
Given the direct evidence for stripping in local analogues(e.g., Cayatte et al.1990; Abramson et al.2011) and at intermediate redshift(Vulcani et al.2015), it seems likely that at least this mechanism is currently operative.
However, to attribute the presence of the smallest-size low-mass passive galaxies—which are also the oldest (Figure9)— to the same mechanism(s), one must take into account the evolution of the cluster itself.
5.2. Small-reLow-mass Passive Cluster Galaxies
At z~3, when many of these logM* M<9.8 systems
seem last to have been in the star-forming population, our target clusters would have been much less massive, and were therefore home to much more tenuous, cooler intra-cluster media. Based on calculations by Trenti et al. (2008),16 we estimate that our clusters—systems withlogMhalo M~15at
~
z 0.5—had progenitors with logMhalo M~14 at z~3
(also consistent with Evrard et al. 2002). Hence, the question becomes whether or not the environments at those epochs— when the global density and neutral gas fraction was higher— Figure 9.Left: size differences, Dlogre, of passive cluster galaxies from thefit for SF galaxies (points). The horizontal lines are sizes of SF galaxies at different
redshifts(printed with T Gyr from the epoch of observation, ~z 0.45; same as blue lines in Figure8) from van der Wel et al. (2014). Median values for each
sub-space are shown by thefilled squares, color-coded by U−V color, which serves as an age indicator. These show redder colors to correspond with larger offsets from the mean size of SF galaxies atz~0.5, suggesting both that(1) smaller-size galaxies are older, and (2) only the largest-size low-mass passive galaxies are consistent with having recently been drawn from the SF population. Right: same as the left but U−V color is corrected for the intrinsic color–mass relation of SF (putative progenitor) galaxies with Equation (6). The color trends along the offset persist after the correction.
Figure 10.Color trend( -U Vcor.) along Dlogrewithin given stellar mass bins
(colored squares), observed in Figure 9. The medians for high-mass (logM* M9.8) and low-mass (logM* M<9.8) galaxies are shown with larger symbols(black and gray, respectively). The error bars are median absolute deviations. We see a bimodal trend at larger Dlogrevalues, where
low-mass galaxies are recently quenched from star-forming population, while at smaller Dlogrevalues the bimodality is converged.
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ΛCDM cosmology based on Wilkinson Microwave Anisotropy Probe (WMAP) year 1 results (Spergel et al.2003).
could have supported ram pressure stripping/significantly cut galaxies off from their fuel supplies. If they could, these channels could provide a unified explanation for all low-mass passive cluster galaxies. If they could not, another channel must have been open.
To further constrain the strength of the above channels and their ability to produce the smallest low-mass passive cluster galaxies, we can assume all of the evolution of the passive fraction shown in Figure 4, bottom-right, is due to the same mechanism(s) and project the effects back in time. We do so by extrapolating a simple linear regression and show the results in Figure 13. This is an extreme model—we might expect the recent evolution in passive fractions to be more rapid than its past evolution due to the cluster and cosmic evolutionary effects mentioned above, which should reduce the efficiency of, e.g., stripping, with lookback time. However, it provides something like an upper limit to the quenching that could be driven by such mechanisms, which is what we seek.
From this exercise, we obtain dFred dt~0.1 Gyr-1 at * <
M M
log 9. By extrapolating the slope, we find that the passive fraction at these masses would be zero at2 z 4, depending on the bin. Hence, it could be that all cluster(core) galaxies with logM* M<9 were transformed due to gas
removal, or whatever other process is active now in clusters.
However, this idea does not hold for even slightly more massive passive cluster galaxies; i.e., those with 9.25
*
M M
log 10.25—there are simply too many of these to have all arisen through the same channel. Our toy calculation suggests that perhaps 30% of these systems were already in place atz~3, just 2Gyr after the Big Bang. For these galaxies
—and presumably those with yet lower masses, assuming a more-realistic nonlinear dFred dt—other explanations must be
sought. We explore one possibility below.
5.2.1. Evidence for Accelerated Evolution for Low-mass Dense Cluster Galaxies
Clues for a formation scenario come from the low-mass passivefield and the high-mass cluster populations.
From Figure 3, we see that, at logM* M<9, there exist
passive galaxies in clusters that are systematically redder than those in thefield. This implies that the cluster galaxies reached theirfinal mass before their field counterparts.
Turning to thelogM* M>9.8 passive cluster population,
Figure8 shows the size–mass relation of these objects to lie remarkably close to a line of constant stellar surface density,
* *
S = M re 2
. This points to common formation time for these systems(Franx et al.2008; van den Bosch et al.2008; Stringer et al.2014; Abramson & Morishita2016; Lilly & Carollo2016; Figure 11.Galaxy sizefitted with stellar mass, Sérsic index, U−V color, local density (S5), and redshift (see Equation (7)). In this fitting, galaxies are not split into any sub-categories(e.g., cluster/field, red/blue).0superscripts in the equation denote that the variables have been normalized to the pivot values(medians) given
below Equation(7). We show the same best fit result in 6panels, but each of which has different color-coded scheme: stellar mass (top left),logn(top middle),
-U V
( )(top right),logS5(bottom left), andlog 1( + z)(bottom middle). The contour of galaxy number density is shown in bottom right. The color is coded according to the observed parameter range shown in each bracket([Min, Max]).
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Whitaker et al. 2016). Given their uniformly ancient stellar populations(based on their colors), the implication is that such high-mass passive cluster galaxies are monolithically old and do not descend from field galaxies transformed over long stretches of time.
Assuming this is the case, we can obtain a rough idea of how many low-mass objects formed similarly by calculating the fraction of them that have surface densities similar to the high-mass systems. Given that the 2-s » 0.5 dex intrinsic spread in sizes at fixed mass corresponds to a factor of 100 difference in surface densities for galaxies at the top and bottom of the size–mass relation, we should expect some
* =
M M
log 8 9– passive objects to have densities compar-able to theirlogM* M=10 11– counterparts.
Indeed, wefind »18% of galaxies withlogM* M<9.8 to
lie above S »* 108.3Mkpc-2—the 1-σ lower bound to the
high-mass sample’s surface densities. This fraction is close to that independently obtained by extrapolating dFred dt above.
Combined with the fact that these systems will, by definition, be the smallest low-mass galaxies—and therefore also the
reddest(Figure9)—this finding strengthens the conclusion that such systems were in place long ago, having formed alongside their massive counterparts. This is consistent with Toloba et al. (2015), who find low-mass galaxies with higher stellar velocity dispersions atfixed mass to have lower reand lie closer to the
cluster core in Virgo, and therefore be older than larger-re
systems.
Notably, the same density calculation reveals only 6% of low-mass field passive galaxies to have densities consistent with high-mass passive objects. If we assume, following Geha et al.(2012), that all of these dense systems are in fact stripped satellites—i.e., they do not truly arise from the same processes generating high-mass passive galaxies—this estimate can be taken as our measurement uncertainty.
As such, combined with the dFred dtresults, we can state the
following: regarding the formation of most high-mass and 10%–30% of logM* M<9.8 passive cluster galaxies,
clusters mark regions of space where evolution was accelerated due to a population’s residence in a common overdensity. That is, perhaps all sufficiently dense cluster galaxies—regardless of stellar mass—are consistent with having arisen through a Figure 12.Parameter estimates for the“holistic size–mass relation” (Equation (7)) fit to the full galaxy sample. Contours reflect 68%, 96%, and 99% confidence
intervals as determined using an MCMC solver. The best fit coefficient values, 50thpercentile, are shown on top of each column with offsets from 16th/ 84thpercentiles. We see no significant degeneracies between the derived coefficients.
